Feed Yeast Market Size

Global Feed Yeast Market size was valued at USD 1.41 billion in 2023 and is poised to grow from USD 1.48 billion in 2024 to USD 2.15 billion by 2032, growing at a CAGR of 4.8% during the forecast period (2025-2032).

The global feed yeast market outlook is experiencing a revolutionary change with increasing focus on sustainable and natural feed alternatives. This change is most evident in the extensive application of feed yeast products as alternatives to traditional antibiotics in animal feed formulations.

• For instance, in March 2024, UK biotechnology company Clean Food Group (CFG) raised Euro 2.5 million to expedite commercialization of its yeast-based, non-GM alternative to palm oil by 2025. The technology holds promise to provide sustainable substitutes in the food industry, showing the scope and variety of yeast-based products beyond traditional applications.

Apart from that, the feed yeast market growth is driven by several technological advancements in the manufacturing process and application methods. Companies are increasingly adopting computerized manufacturing systems and applying quality control measures in order to maintain consistent product quality. The sector has witnessed amazing developments in fermentation technologies and processes, leading to more efficient feed yeast product manufacturing. These advancements in technology have enabled manufacturers to meet growing demand without compromising on product quality and effectiveness. The industry is also witnessing product formulation breakthroughs, with manufacturers producing specialty blends that combine different yeasts and their derivatives to deliver the highest nutritional benefits.

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With greater manufacturing efficiency, product creativity, and quality regulation, artificial intelligence (AI) is increasingly altering the feed yeast industry. Artificial intelligence (AI) systems make use of gigantic amounts of information from fermentation activities to optimize temperature, pH levels, and concentration of nutrients in real-time for better yeasts and product maintenance. Batch failure is reduced and a proactive nature enabled by machine learning algorithms that detect and identify patterns in microbial growth.

• For instance, Lallemand Inc. introduced AI-driven image analysis in October 2024 to streamline yeast fermentation quality control. Lallemand's Tallinn, Estonia, R&D center utilized automated AI models in conjunction with Aiforia to quickly detect microbial contamination within yeast cells. Batch failures were reduced and active adjustments enabled by the tremendous decrease in time to detect contamination offered by this new process. Lallemand enables the industry to shift towards natural and sustainable sources of feed while ensuring product quality consistency through the use of AI.